PhD Candidate in Advanced Shape Memory Materials & Additive Manufacturing

About the role

This PhD project investigates process‑structure‑property relationships in advanced shape memory alloys and polymers produced by additive manufacturing. The aim is to develop a data‑driven framework for designing functionally graded smart materials for 4D printing applications.

Key responsibilities

• Design and execute additive manufacturing trials (LPBF/SLM, SLS, FDM) for shape memory alloys and polymers.
• Characterize thermal, mechanical, microstructural and shape‑memory properties using DSC, SEM, XRD, DMA, etc.
• Develop machine‑learning models to map multi‑parameter manufacturing spaces to material responses.
• Analyse experimental and computational data to drive multi‑material and multi‑process innovation.
• Publish at least four first‑author peer‑reviewed journal articles and present findings at international conferences.

Required profile

• Master’s degree in Materials Science, Mechanical Engineering, Polymer Engineering, Additive Manufacturing or related field.
• Strong background in materials science, physical metallurgy, polymer science or smart materials.
• Practical or theoretical knowledge of SLM/LPBF, SLS or FDM techniques.
• Interest or experience in machine learning, data analysis and computational materials modeling.
• Proficiency in English (IELTS/TOEFL certificates desirable) and strong scientific writing skills.

Required skills

• Additive manufacturing (LPBF/SLM, SLS, FDM)
• Materials characterization (DSC, SEM, XRD, DMA)
• Machine learning and data‑driven modeling
• Python or similar programming for data analysis